National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Machine Learning Engineer

City of London
2 weeks ago
Create job alert

Job Title: Machine Learning Engineer

Location: London (1 days per week onsite) - Flexible

Salary: £45,000 DOE + Benefits

Our Data Analytics business continues to grow and we are now looking for an experienced and technical Machine Learning (ML) Engineer to join one our offices with hybrid or remote UK working. This is an exciting role and would most likely suit someone with previous experience in a similar role where they have gained knowledge and experience of designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments. You must have good technical knowledge of Phyton, SQL, CI/CD and familiar with Power BI.

A FTSE 250 company, they combine expertise and insight with advanced technology and analytics to address the needs of over 1,400 schemes and their sponsoring employers on an ongoing and project basis. We undertake administration for over one million members and provide advisory services to schemes and corporate sponsors in respect of schemes of all sizes, including 88 with assets over £1bn. We also provide wider ranging support to insurance companies in the life and bulk annuities sector.

The Team

The client is a specialist and multi-disciplinary team consisting of actuaries, data scientist and developers. Our role in this mission is to pioneer advancements in the field of pensions and beyond, leveraging state-of-the-art technology to extract valuable and timely insights from data. This enables the consultant to better advise Trustees and Corporate clients on a wide range of actuarial-related areas.

The Role

As a Machine Learning Engineer you will:

Model development. Work collaboratively with actuarial analysts to develop machine learning and statistical models to predict outcomes, related to pension schemes, such as life expectancy, default risk, or investment returns. Identify appropriate machine learning algorithms and apply them to enhance predictions, automate decision-making processes, and improve client offerings.
Machine Learning Operations. Responsible for designing, deploying, maintaining and refining statistical and machine learning models using Azure ML. Optimize model performance and computational efficiency. Ensure that applications run smoothly and handle large-scare data efficiently. Implement and maintain monitoring of model drifts, data-quality alerts, scheduled r-training pipelines.
Data Management and Preprocessing. Collect, clean and preprocess large datasets to facilitate analysis and model training. Implement data pipelines and ETL processes to ensure data availability and quality.
Software Development. Write clean, efficient and scalable code in Python. Utilize CI/CD practices for version control, testing and code review.
Work closely with actuarial analysts, actuarial modelling team (AMT) and other colleagues to integrate data science findings into practical advice and strategies.
Stay abreast of new trends and technologies in Data Science technologies and pensions to identify opportunities for innovation.
Provide training and support to other team members on using machine learning tools and understanding analytical techniques.
Interpret and explain machine learning concepts and findings to other members of the analytics team and non-technical stakeholders.

Your profile

Essential Criteria

Previous experience in designing, building, optimising, deploying and managing business-critical machine learning models using Azure ML in Production environments.
Experience in data wrangling using Python, SQL and ADF.
Experience in CI/CD and DevOps/MLOps and version control.
Familiarity with data visualization and reporting tools, ideally PowerBI.
Good written and verbal communication and interpersonal skills. Ability to convey technical concepts to non-technical stakeholders.
Experience in the pensions or similar regulated financial services industry is highly desirable.
Experience in working within a multidisciplinary team would be beneficial.

We offer an attractive reward package, typical benefits can include:

Competitive salary
Participation in annual discretionary Bonus Scheme
25 days holiday plus flexibility to buy or sell holiday
Flexible Bank holidays
Pension scheme, matching contribution structure
Healthcare cash plan
Flexible Benefits Scheme to support you in and out of work, helping you look after you and your family covering Security & Protection, Health & Wellbeing, Lifestyle
Life Assurance cover, four times basic salary
Rewards (offers High Street discounts and savings from retailers and services providers as well as offers available via phone)
Employee Assistance Programme for you and your household
Access to a digital GP service
Paid volunteering day when participating in Company organised events
Staff referral scheme when you introduce a friend

In Technology Group Ltd is acting as an Employment Agency in relation to this vacancy

Related Jobs

View all jobs

Machine Learning Engineer

Software Developer

Machine Vision Internship – Paid AI Opportunity

AI Trainer

Site Reliability Engineer - Graduate Considered

Data Scientist - Graduate

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Get a Better Data Engineering Job After a Lay-Off or Redundancy

Redundancy can be unexpected and unsettling, especially in a field as technically demanding as data engineering. But the good news is: your skills are still in high demand. The UK continues to see strong growth in data infrastructure, cloud analytics, machine learning pipelines, and data governance roles. Whether you're a big data engineer, ETL specialist, cloud data platform expert, or someone working in real-time streaming and pipelines, there are new opportunities to rebuild and rebrand your career. This guide is designed to help UK-based data engineers bounce back after a redundancy, with a step-by-step roadmap to relaunch into a stronger, better-aligned role.

Data Engineering Jobs Salary Calculator 2025: Work Out Your True Worth in Seconds

Why last year’s pay survey misleads data engineers today Ask any Data Engineer elbow‑deep in late‑arriving CDC streams, an Analytics Engineer stockpiling dbt models, or a DataOps Lead juggling Airflow failures: “Am I earning what I deserve?” The answer changes monthly. New GPU‑hungry AI workloads spike storage costs, lakehouse toolchains displace legacy marts, & suddenly real‑time streaming isn’t “nice to have” but the lion’s share of your backlog. Each shift nudges salary bands. A PDF salary guide printed in 2024 under‑reports pay the moment Databricks announces another acquisition or HMRC mandates digital provenance. To provide an up‑to‑date benchmark, DataEngineeringJobs.co.uk distilled a transparent, three‑factor formula. Plug in your discipline, UK region, & seniority; out pops a realistic 2025 salary. No stale averages, no guesswork. This article unpacks the formula, details the forces pushing data‑engineering pay upward, & offers five practical actions to lift your value in the next ninety days.

How to Present Data Engineering Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

As the demand for data engineers grows, so do the expectations. It’s not enough to build robust pipelines or optimise ETL jobs—UK employers now look for candidates who can also communicate clearly with stakeholders, especially those without technical backgrounds. Whether you're applying for a data engineering role in finance, healthcare, retail, or tech, your ability to explain complex systems in plain English is becoming one of the most valued soft skills in interviews and in the workplace. This guide will help you master public speaking for data engineering roles: from structuring your presentation and designing effective visuals, to simplifying terminology, storytelling and confidently answering stakeholder questions.